Vehicle searching (also known as vehicle re-identification) refers to searching, given an image of a target vehicle, a vehicle monitoring image database for vehicle images that are similar to a image of a target vehicle according to visual features and time-space information of the image of the target vehicle and the like, and arranging the similar vehicle images found in an ascending order of the similarity.
Vehicle searching has very important application value in urban traffic control, such as traffic violation management, traffic flow statistics, urban management and so on.
Currently, there are mainly two types of vehicle searching methods. The first type of vehicle searching method is a vehicle searching method based on license plate recognition at accesses in constrained scenes, wherein target vehicle searching is realized mainly by comparing images of the target vehicle with all images in a vehicle monitoring image database according to the license plate information to obtain similarity therebetween. This method requires to detect and acquire vehicle images containing license plate information in the scenes in which the license plate images can be collected conveniently, such as accesses of roads and parking lot, road intersections, and to acquire license plate information in the vehicle images using license plate recognition technology. The second type of vehicle searching method is a similar vehicle searching method based on vehicle appearance features in unconstrained scenes. In the similar vehicle searching method, target vehicle searching is realized mainly by comparing an image of a target vehicle with all images in a vehicle monitoring image database according to the appearance visual features of a vehicle to obtain similarity therebetween, the appearance visual features of a vehicle including color, shape, size, texture and other information.
Although the first method can improve the accuracy of vehicle searching, it is limited to a license plate information recognition system, and is only adapted to constrained scenes, such as accesses of roads and parking lot, road intersections and the like, but is not adapted to unconstrained scenes. Moreover, license plate information recognition has a relatively low efficiency in large-scale video surveillance scenes, and imposes higher requirements on hardware such as cameras that collect images of a vehicle and auxiliary devices. The second method has no constraint on application scenes; however, the accuracy of searching a target vehicle is reduced.
In view of above, there is an urgent need for a vehicle searching method that is not limited by application scenes while improving searching accuracy and searching speed.